Chatting new territory: large language models for infection surveillance from pilot to deployment. Journal Articles uri icon

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abstract

  • Rodriguez-Nava et al. present a proof-of-concept study evaluating the use of a secure large language model (LLM) approved for healthcare data for retrospective identification of a specific healthcare-associated infection (HAI)-central line-associated bloodstream infections-from real patient data for the purposes of surveillance.1 This study illustrates a promising direction for how LLMs can, at a minimum, semi-automate or streamline HAI surveillance activities.

authors

publication date

  • February 14, 2025